The Ultimate Glossary of Artificial Intelligence Terms

What is Artificial Intelligence? Great question – and one we get asked a lot.

The definition of artificial intelligence (AI) differs depending on who you ask. Why? Because there are many different types of AI, for many different purposes. In Phrasee’s Glossary of Artificial Intelligence terms, you’ll learn what all the key terms mean.

So fear not! When the machines come knocking on your door, you, for one, will be able to welcome your new robot overlords.

Also known as bots, droids or intelligent agents, these agents are autonomous software programs that respond to their environment and act on the behalf of humans to accomplish a system’s target function. When multiple agents are used together in a system, they interact with one another to achieve the goals of the overall system.

Computer programs designed to solve difficult problems which humans (and animals) routinely solve. The goal of AI is to develop programs which can solve such problems independently, although the patterns for solving these problems differ significantly from the way they are solved by humans.

A model that represents and calculates the probabilistic relationships between a set of random variables and an uncertain domain via a directed acyclic graph. The nodes on the graph represent the random variables and the links between them represent their conditional dependencies.

For example, a Bayesian network can be used to calculate the probabilities of various diseases being present (the uncertain domain) based on the given symptoms (the variables).

A fundamental problem in computing whereby the number of combinations that a computer has to examine grows exponentially. The number of combinations can become so large that even the fastest computers aren’t able to examine them all in a conceivable time frame (we are talking hundreds of thousands of years here!).

A multidisciplinary research area that draws on the fields of art, science, philosophy and artificial intelligence to engineer computational systems that are able to model, stimulate and replicate human creativity.

For example, IBM researchers are exploring how computational creativity can be used in the food industry to make recipes for dishes that have never been imagined before by humans.

The process of combing through a data set to identify patterns and extract information. Often such patterns and information are only clear when a large enough data set is analysed. For this reason, AI and machine learning are extremely helpful in such a process.

A model that uses prescriptive analytics to establish the best course of action for a given situation. The model assesses the relationships between the elements of a decision to recommend one or more possible courses of action. It may also predict what should happen if a certain action is taken.

A subset of AI and Machine learning in which Neural networks are “layered”, combined with plenty of computing power, and given a large measure of training data to create extremely powerful learning models capable of processing data in new and exciting ways in a number of areas, e.g. advancing the field of computer vision.

A summary of a dataset that describes its main features and quantifies relationships in the data. Some common measures used to describe a data set are measures of central tendency (mean, median and mode).

A method for solving optimisation problems by mimicking the process of natural selection and biological evolution. The algorithm randomly selects pairs of individuals from the population (whereby the best performing individuals are more likely to be chosen) to be used as parents. These individuals are then crossed over to create a new generation of two individuals, or children. This process is repeated until the optimisation problem is solved.

A subgenre of AI in which computer programs and algorithms can be designed to “learn” how to complete a specified task, with increasing efficiency and effectiveness as it develops. Such programs can use past performance data to predict and improve future performance.

A machine learning task in which an algorithm attempts to generate language that is comprehensible and human-sounding. The end goal is to produce computer-generated language that is indiscernible from language generated by humans.

A machine learning task concerned with improving the interaction between humans and computers. This field of study focuses on helping machines to better understand human language in order to improve human-computer interfaces.

A computer system that takes images of typed, handwritten or printed text and converts them into machine-readable text. For example when you deposit a cheque into a bank machine, OCR software is used to recognise the information written on the cheque.

A machine learning problem whereby an algorithm is unable to discern information relevant to its assigned task from information which is irrelevant to its assigned task within training data. Overfitting therefore inhibits the algorithm’s predictive performance when dealing with new data.

Any characteristic that can be used to help define or classify a system such as as event, thing, person, project or situation. In AI, parameters are used to clarify exactly what an algorithm should be seeking to identify as important data when performing its target function.

The process of removing sections of decision trees that provide little power to classify instances (the occurrence of something). This technique reduces the size and complexity of the final decision tree and improves accuracy by eliminating the parts of the tree that are likely to cause overfitting.

A type of artificial neural network in which recorded data and outcomes are fed back through the network forming a cycle. This process allows the network to use its internal memory to sort through random data as it goes.

A type of machine learning in which machines are “taught” to achieve their target function through a process of experimentation and reward. In reinforcement learning, the machine receives positive reinforcement when its processes produce the desired result, and negative reinforcement when they do not.

A type of machine learning in which human input and supervision are an integral part of the machine learning process on an ongoing basis. In supervised learning, there is a clear outcome to the machine’s data mining and its target function is to achieve this outcome, nothing more.

An approach to artificial intelligence that is based on the idea that when individual agents come together, the interactions between them lead to the emergence of a more ‘intelligent’ collective behaviour. It stems from the natural behaviour of animals such as bees, which combine into swarms to work more intelligently.

In machine learning, the test data set is the data given to the machine after the training and validation phases have been completed. The test data set is used to check the performance characteristics of the algorithms produced after the completion of the first two phases when presented with unknown data. This will give a good indication of the accuracy, sensitivity and specificity of the algorithm’s predictive powers.

In machine learning, the training data set is the data given to the machine during the initial “learning” or “training” phase. From this data set the machine is meant to gain some insight into options for the efficient completion of its assigned task through identifying relationships between the data.

A type of machine learning in which human input and supervision are extremely limited, or absent altogether, throughout the process. In unsupervised learning, the machine is left to identify patterns and draw its own conclusions from the data sets it is given.

In machine learning, the validation data set is the data given to the machine after the initial learning phase has been completed. The validation data is used to identify which of the relationships identified during the learning phase will be the most effective to use in predicting future performance